A higher penetration of renewable energy resources (RERs) in power system networks introduces uncertainty in a grid, which causes an unbalanced load flow and transient stability issues in the power system. If these critical problems are not handled properly, then it will lead the network to an instability state, and thus causing cascading overload failures. Therefore, proper assessment of renewable integrated power grids (RIPGs) is required for load flow balancing and transient stability to overcome these instability issues. These issues are even more challenging in case of the occurrence of multiple interval symmetrical or asymmetrical faults, which arise in power systems due to power quality disturbances. The effect of these multiple interval faults is more severe than a single interval fault, and thus it can lead the network to an instability state in a short span of time. Therefore, in this case, some remedial action must be executed quickly, before the net work entirely moves to an unstable state. This work intends to examine the influence of multiple interval faults on RERs for load flow balancing and transient stability analysis to solve the problem of network instabilities in the power system, which leads to cascading failure outages. For this purpose, probabilistic modeling is proposed in this research work for load flow balancing and transients stability enhancement in RIPGs. For load flow balancing, a transmission network topology based on a smart node is utilized along with an integration of a unified power flow controller (UPFC), while transient stability is assessed through a UPFC alone. For the enhancement of computational speed, a technique based on self-propagating graph was utilized. Through this, a network operators can easily detect critical nodes by rooting straight to the vulnerable points, thus providing stability to the grid in a short span of time. Contrary to the previously proposed algorithms, this work is supported by probabilistic modeling to mitigate network instabilities due to an arising of unbalanced load flow and transients stability issues in case of occurrence of multiple intervals faults in RIPGs. Simulation results show that the proposed algorithm quantifies the existing alternatives and can achieve near optimal performance for a wide range of load variations and power quality disturbances in RIPGs.

Policies aim to increase the penetration of renewable energy resources (RERs) to minimize the dependence on fossil fuels, has compelled different countries to devise their diverse blueprints by transforming smart grids infrastructure into futuristic grids or super smart grids (SSGs). Europe is taking the lead by developing SSGs by 2050, based on two exclusive alternatives in the form of a wide area network and decentralized power generation using many RERs. Critically assessing tensions in balancing load flow associated with RERs is a challenging research issue in SSGs. To address this problem, the advanced probabilistic model is adopted to observe the variations in demand and response proles and mitigate it through transmission network planning using a super smart node transmission network topology (SSN). Future contingencies are easily predictable, before any disruptive changes arise in SSGs by incorporating these probabilistic analyses in SSGs. The proposed SSN transmission network topology outperforms the power synergy hub (PSHub) topology used in the existing literature through achieving an optimal load flow balancing in SSGs power infrastructure. Finally, this research work also provides a comprehensive overview of technical challenges in SSGs in terms of power control and load flow balancing, their possible solutions, and future prospects.